192 Mucciardi et al.: Vibration Waveforms as an Initial Decay Detection Tool that used speed, pulse height, and other wave properties in combination with electrical impedance. Other investigators have since reported on ring of sensor-based methods (Nico- lotti et al. 2003; Gilbert and Smiley 2004; Bucur 2005; Wang et al. 2005; Wang et al. 2008). The sonic tomography instru- ments have shown increased resolution of internal defects at the expense of operational simplicity and increased costs. The Axmon et al. (2004) multipath system represents some- what of a balance between the simple single-path timing instru- ments and the more complex tomography systems. Although the Axmon et al. instrument required additional equipment (using a ring of accelerometer sensors), it also incorporated a relatively simple decay detection logic based on frequency and surface wave propagation velocity. The system successfully identified decay in living stands of Picea abies with an accuracy of ap- proximately 74%. These promising results were limited to this single species, however, and the authors concluded that their models would likely have to be recalibrated for each new stand. This research study was conducted to determine the feasi- bility of using a relatively simple electro-mechanical instru- ment for decay detection based on statistical classification of a stress-induced sonic waveform recorded from a single ac- celerometer sensor, as opposed to velocity-based stress wave timing. This system extracted statistical features that were simi- lar to the acoustic cues used to train human operators during a sounding evaluation. Like the aforementioned nondestruc- tive detection instruments, it would be invariant to an opera- tor’s experience level, so that different operators would obtain the same results. Unlike the multipath measurement instru- ments, however, this system would have the benefit of requir- ing only a relatively simple and inexpensive set of hardware components, as well as generalizing to detect decay across a wide variety of urban tree species without repeated calibration. The theoretical basis for measuring characteristic acoustic structures with statistical features and classifying the contents of a trunk based on those feature values is built on the basic principles of vibration wave propagation and psychoacoustic research. When a hammer strikes the trunk, a stress wave is generated that propagates through the wood matrix at a veloc- ity proportional to the square root of Young’s modulus, which depends on the physical properties of the wood. The stress wave moves through the medium by alternating pulses of pressure waves that travel parallel to the trunk surface with a wave pe- riod (wavelength) determined by the wave’s velocity. Internal discontinuities of the medium alter the velocity and amplitude of the wave. When it reaches the surface and causes it to vibrate, the vibrations generate pressure waves in the surrounding air that are perceived as sound when these airborne pressure waves strike an eardrum. The perceived pitch of these sounds is depen- dent upon the trunk reverberations and, thus, by its internal con- dition. Due to the mass of the trunk, the frequency of the pitch falls in the normal audible range (approximately 50 to 4,000 Hz). Although psychoacoustic research does not specifically dis- cuss wood decay sounds, it has shown that the human brain can distinguish many other complex natural sounds (Howard 1977; Howard and Ballas 1980; Ballas 1993; Miller 1994), including footfalls (Li et al. 1991), slamming doors (Fowler and Rosen- blum 1990), clapping hands (Repp 1987), and breaking bottles (Warren and Verbrugge 1984). Notably, Freed (1990) found that there are acoustic differences between percussive events ©2011 International Society of Arboriculture like mallet strikes, and Lufti (2001) found that listeners can de- velop strategies for distinguishing solid and hollow metal and wooden bars. The sounds considered in these studies contained sufficient information to allow identification of complex source attributes (analogous to decay), and moreover, that listeners are capable of performing these identifications with limited variation in other source attributes (analogous to diameter and species). The authors studied various statistical features to determine which combination comprised sufficient information to successfully dis- tinguish sounds from decayed versus non-decayed tree samples. As in traditional engineering literature, features that described various aspects of the waveform’s acoustic structure were explored (Stearns 1976; Shin and Kil 1996; Vapnik 1998; Wellman and Srour 1999). To demonstrate the feasibility of using an acoustic waveform classification system to detect decay across a range of urban tree spe- cies, the study authors assembled a test bed consisting of a variety of recently cut tree sections across a wide range of commonly found urban tree species, trunk diameter, and internal conditions (solid, advanced decay, and hollow). A simple accelerometer-based data collection system and applied signal processing, waveform feature extraction, and pattern classification algorithms were configured to the test bed samples to estimate the classifier’s performance. MATERIALS AND METHODS Sample Selection Twenty felled urban trees were collected in Maryland, U.S. The trunks were further divided with a chain saw if visual assessment indicated that different levels of decay were present. Although limited vibration differences may result from using felled trunk segments instead of standing trees, the authors had to rely on these segments as the only available test data, as have similar studies (Nicolotti et al. 2003; Wang et al. 2005). The final data set consisted of 36 sections of 12 different tree species, a much wider range of species than similar studies (Axmon et al. 2004; Gilbert and Smiley 2004; Nicolotti et al. 2003; Wang et al. 2005; Wang and Allison 2008; Kazemi-Najafi et al. 2009), as shown in Table 1. Tree species included in the sample were tulip poplar (Lirioden- dron tulipifera), red maple (Acer rubrum), American elm (Ulmus americana), paulownia (Paulownia tomentosa), sycamore (Plata- nus occidentalis), ash (Fraxinus spp.), white oak (Quercus alba), American beech (Fagus grandifolia), white pine (Pinus strobus), white cedar (Thuja occidentalis), red oak (Quercus rubra), and black locust (Robinia pseudoacacia). We did not measure the den- sity of our samples, but the species represented a wide range of wood densities (United States Department of Agriculture 2007). The samples had the majority of their bark still intact and ranged in length from 58.4 cm to 221 cm and diameter from 32.5 cm to 99.1 cm. The condition of each section was visu- ally assessed and labeled as either “decayed” (having visible decay or hollows) or “non-decayed” (solid) (Luley 2006). The test bed and example cross-sections are shown in Figure 1. Measurement Equipment The data collection system was composed of an accelerom- eter (PCB Piezotronics, Inc., Shear Accelerometer, Model 353B33) with a frequency range of 1 to 4,000 Hz, a load cell hammer (PCB Piezotronics, Inc., Load Cell Hammer, Model
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